“Carotenoid analysis of cassava genotypes roots (Manihot esculenta Crantz) cultivated in southern Brazil using chemometric tools” Author: “Moresco, R.(2014)” Date: “Thursday, January 22, 2015”
R script for Analysis of HPLC and UV-Visible Spectrophotometric Data
Reading data and metadata
setwd("/Users/Windows/Desktop/Miguel/Metabolomics-package")
source("http://bioconductor.org/biocLite.R")
source("scripts/init.R")
uv.cassava.metadata.file = "Datasets/CassavaCultivars/UVV/metadata/cass_uv_metadata.csv"
uv.cassava.data.file = "Datasets/CassavaCultivars/UVV/data/uvv-cassava.csv"
label.x = "wavelength(nm)"
label.val = "absorbance"
uv.cassava.ds = read.dataset.csv(uv.cassava.data.file, uv.cassava.metadata.file,
description = "UV data for cassava", type = "uvv-spectra", format = "col",
label.x = label.x, label.values = label.val)Preliminary Inspection of Data
sum.dataset(uv.cassava.ds)## Dataset summary:
## Valid dataset
## Description: UV data for cassava
## Type of data: uvv-spectra
## Number of samples: 30
## Number of data points 501
## Number of metadata variables: 3
## Label of x-axis values: wavelength(nm)
## Label of data points: absorbance
## Number of missing values in data: 0
## Mean of data values: 0.1605
## Median of data values: 0.01487
## Standard deviation: 0.4252
## Range of values: -0.1068 2.722
## Quantiles:
## 0% 25% 50% 75% 100%
## -0.1067503 -0.0005422 0.0148732 0.0888727 2.7218540Get metadata
get.metadata(uv.cassava.ds)## varieties colors replicates
## Apronta mesa_1 Apronta.mesa cream 1
## Apronta mesa_2 Apronta.mesa cream 2
## Apronta mesa_3 Apronta.mesa cream 3
## Pioneira_1 Pioneira yellow 1
## Pioneira_2 Pioneira yellow 2
## Pioneira_3 Pioneira yellow 3
## Oriental_1 Oriental cream 1
## Oriental_2 Oriental cream 2
## Oriental_3 Oriental cream 3
## Amarela_1 Amarela yellow 1
## Amarela_2 Amarela yellow 2
## Amarela_3 Amarela yellow 3
## Catarina_1 Catarina yellow 1
## Catarina_2 Catarina yellow 2
## Catarina_3 Catarina yellow 3
## IAC 576-70_1 IAC.576.70 yellow 1
## IAC 576-70_2 IAC.576.70 yellow 2
## IAC 576-70_3 IAC.576.70 yellow 3
## Salezio_1 Salezio cream 1
## Salezio_2 Salezio cream 2
## Salezio_3 Salezio cream 3
## Estacao_1 Estacao cream 1
## Estacao_2 Estacao cream 2
## Estacao_3 Estacao cream 3
## Videira_1 Videira cream 1
## Videira_2 Videira cream 2
## Videira_3 Videira cream 3
## Rosada_1 Rosada red 1
## Rosada_2 Rosada red 2
## Rosada_3 Rosada red 3USING FULL UV-VISIBLE DATA (200-700 nm)
plot.spectra(uv.cassava.ds,"varieties")
Data Pre-Processing
Smoothing and baseline correction
uv.cassava.wavelens = get.x.values.as.num(uv.cassava.ds)
x.axis.sm = seq(min(uv.cassava.wavelens), max(uv.cassava.wavelens),10)
uv.cassava.smooth = smoothing.interpolation(uv.cassava.ds, method = "loess", x.axis = x.axis.sm)
plot.spectra(uv.cassava.smooth, "varieties")
uv.cassava.bg = data.correction(uv.cassava.smooth,"background")
uv.cassava.offset = data.correction(uv.cassava.bg, "offset")
uv.cassava.baseline = data.correction(uv.cassava.offset, "baseline")
sum.dataset(uv.cassava.baseline)## Dataset summary:
## Valid dataset
## Description: UV data for cassava-smoothed with hyperSpec spc.loess; background correction; offset correction; baseline correction
## Type of data: undefined
## Number of samples: 30
## Number of data points 51
## Number of metadata variables: 3
## Label of x-axis values: wavelength(nm)
## Label of data points: absorbance
## Number of missing values in data: 0
## Mean of data values: 0.08889
## Median of data values: 0.02441
## Standard deviation: 0.1923
## Range of values: -0.0002181 1.29
## Quantiles:
## 0% 25% 50% 75% 100%
## -0.0002181 0.0076378 0.0244131 0.0764408 1.2895338plot.spectra(uv.cassava.baseline, "varieties")
UNIVARIATE ANALYSIS
uv.cassava.baseline.anova = univariate.analysis(uv.cassava.baseline, type = "anova", "varieties")
uv.cassava.baseline.anova[1:10,]## pvalues logs fdr
## 500 1.073e-18 17.97 5.470e-17
## 470 4.485e-18 17.35 1.144e-16
## 490 1.394e-17 16.86 2.083e-16
## 460 1.642e-17 16.78 2.083e-16
## 510 2.043e-17 16.69 2.083e-16
## 480 6.334e-17 16.20 5.384e-16
## 290 7.563e-17 16.12 5.510e-16
## 440 3.299e-16 15.48 2.103e-15
## 450 4.829e-16 15.32 2.736e-15
## 300 1.322e-15 14.88 6.740e-15
## tukey
## 500 Rosada-Amarela; Rosada-Apronta.mesa; Rosada-Catarina; Rosada-Estacao; Rosada-IAC.576.70; Rosada-Oriental; Rosada-Pioneira; Salezio-Rosada; Videira-Rosada
## 470 Rosada-Amarela; Rosada-Apronta.mesa; Rosada-Catarina; Rosada-Estacao; Rosada-IAC.576.70; Rosada-Oriental; Rosada-Pioneira; Salezio-Rosada; Videira-Rosada
## 490 Rosada-Amarela; Rosada-Apronta.mesa; Rosada-Catarina; Rosada-Estacao; Rosada-IAC.576.70; Rosada-Oriental; Rosada-Pioneira; Salezio-Rosada; Videira-Rosada; Videira-Salezio
## 460 Rosada-Amarela; Catarina-Apronta.mesa; Pioneira-Apronta.mesa; Rosada-Apronta.mesa; Oriental-Catarina; Rosada-Catarina; Salezio-Catarina; Rosada-Estacao; Oriental-IAC.576.70; Rosada-IAC.576.70; Salezio-IAC.576.70; Pioneira-Oriental; Rosada-Oriental; Rosada-Pioneira; Salezio-Pioneira; Salezio-Rosada; Videira-Rosada
## 510 Rosada-Amarela; Rosada-Apronta.mesa; Rosada-Catarina; Rosada-Estacao; Rosada-IAC.576.70; Rosada-Oriental; Rosada-Pioneira; Salezio-Rosada; Videira-Rosada
## 480 Rosada-Amarela; Rosada-Apronta.mesa; Rosada-Catarina; Salezio-Catarina; Rosada-Estacao; Rosada-IAC.576.70; Rosada-Oriental; Rosada-Pioneira; Salezio-Rosada; Videira-Rosada
## 290 Apronta.mesa-Amarela; IAC.576.70-Amarela; Oriental-Amarela; Pioneira-Amarela; Salezio-Amarela; Videira-Amarela; Catarina-Apronta.mesa; Estacao-Apronta.mesa; Pioneira-Apronta.mesa; Rosada-Apronta.mesa; Videira-Apronta.mesa; IAC.576.70-Catarina; Oriental-Catarina; Pioneira-Catarina; Salezio-Catarina; Videira-Catarina; IAC.576.70-Estacao; Oriental-Estacao; Pioneira-Estacao; Salezio-Estacao; Videira-Estacao; Pioneira-IAC.576.70; Rosada-IAC.576.70; Videira-IAC.576.70; Pioneira-Oriental; Rosada-Oriental; Videira-Oriental; Rosada-Pioneira; Salezio-Rosada; Videira-Rosada
## 440 Apronta.mesa-Amarela; Estacao-Amarela; Oriental-Amarela; Rosada-Amarela; Catarina-Apronta.mesa; IAC.576.70-Apronta.mesa; Pioneira-Apronta.mesa; Rosada-Apronta.mesa; Estacao-Catarina; Oriental-Catarina; Rosada-Catarina; Salezio-Catarina; IAC.576.70-Estacao; Pioneira-Estacao; Rosada-Estacao; Oriental-IAC.576.70; Rosada-IAC.576.70; Salezio-IAC.576.70; Pioneira-Oriental; Rosada-Oriental; Rosada-Pioneira; Salezio-Pioneira; Salezio-Rosada; Videira-Rosada
## 450 Oriental-Amarela; Rosada-Amarela; Salezio-Amarela; Catarina-Apronta.mesa; IAC.576.70-Apronta.mesa; Pioneira-Apronta.mesa; Rosada-Apronta.mesa; Estacao-Catarina; Oriental-Catarina; Rosada-Catarina; Salezio-Catarina; IAC.576.70-Estacao; Pioneira-Estacao; Rosada-Estacao; Oriental-IAC.576.70; Rosada-IAC.576.70; Salezio-IAC.576.70; Pioneira-Oriental; Rosada-Oriental; Rosada-Pioneira; Salezio-Pioneira; Salezio-Rosada; Videira-Rosada
## 300 Apronta.mesa-Amarela; IAC.576.70-Amarela; Oriental-Amarela; Pioneira-Amarela; Salezio-Amarela; Videira-Amarela; Catarina-Apronta.mesa; Estacao-Apronta.mesa; Pioneira-Apronta.mesa; Rosada-Apronta.mesa; Videira-Apronta.mesa; IAC.576.70-Catarina; Oriental-Catarina; Pioneira-Catarina; Salezio-Catarina; Videira-Catarina; IAC.576.70-Estacao; Oriental-Estacao; Pioneira-Estacao; Salezio-Estacao; Videira-Estacao; Pioneira-IAC.576.70; Rosada-IAC.576.70; Videira-IAC.576.70; Pioneira-Oriental; Rosada-Oriental; Videira-Oriental; Rosada-Pioneira; Salezio-Pioneira; Salezio-Rosada; Videira-Rosada; Videira-SalezioHierarchical Cluster Analysis
Using Euclidian Distance
uv.cassava.hc = clustering(uv.cassava.ds, method = "hc", distance = "euclidean")
dendrogram.plot(uv.cassava.ds, uv.cassava.hc)
dendrogram.plot.col(uv.cassava.ds, uv.cassava.hc, "varieties")
Principal Components Analysis
Importance of components: Proportion of Variance explained in each component
uv.cassava.pca = pca.analysis.dataset(uv.cassava.ds)
summary(uv.cassava.pca)## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Standard deviation 17.129 10.087 8.289 4.249 3.0243 1.77968 1.31468
## Proportion of Variance 0.586 0.203 0.137 0.036 0.0183 0.00632 0.00345
## Cumulative Proportion 0.586 0.789 0.926 0.962 0.9802 0.98651 0.98996
## PC8 PC9 PC10 PC11 PC12 PC13
## Standard deviation 0.99687 0.84476 0.77221 0.72841 0.63003 0.47799
## Proportion of Variance 0.00198 0.00142 0.00119 0.00106 0.00079 0.00046
## Cumulative Proportion 0.99194 0.99337 0.99456 0.99562 0.99641 0.99686
## PC14 PC15 PC16 PC17 PC18 PC19
## Standard deviation 0.43455 0.41855 0.40385 0.36874 0.33393 0.32915
## Proportion of Variance 0.00038 0.00035 0.00033 0.00027 0.00022 0.00022
## Cumulative Proportion 0.99724 0.99759 0.99792 0.99819 0.99841 0.99863
## PC20 PC21 PC22 PC23 PC24 PC25
## Standard deviation 0.32294 0.3174 0.29612 0.28055 0.26165 0.25277
## Proportion of Variance 0.00021 0.0002 0.00018 0.00016 0.00014 0.00013
## Cumulative Proportion 0.99883 0.9990 0.99921 0.99937 0.99950 0.99963
## PC26 PC27 PC28 PC29 PC30
## Standard deviation 0.23929 0.2257 0.20367 0.18610 7.61e-15
## Proportion of Variance 0.00011 0.0001 0.00008 0.00007 0.00e+00
## Cumulative Proportion 0.99975 0.9999 0.99993 1.00000 1.00e+00Robust and centralized pca (3D and 2D)
pca.scoresplot2D(uv.cassava.ds, uv.cassava.pca, "varieties", ellipses=T)
pca.scoresplot2D(uv.cassava.ds, uv.cassava.pca, "colors", ellipses=T, pallette=2)
pca.scoresplot2D(uv.cassava.ds, uv.cassava.pca, "varieties", ellipses="F")
pca.scoresplot2D(uv.cassava.ds, uv.cassava.pca, "colors", ellipses="T", pallette=2, labels="T")
pca.scoresplot2D(uv.cassava.ds, uv.cassava.pca, "colors", ellipses="T", pallette=2, labels="F")
CAROTENOIDS FINGERPRINT REGION (400-500 nm)
Carotenoids have absorption maxima in the UV-visible region of 450 nm
uv.cassava.carot = subset.x.values.by.interval(uv.cassava.ds, min.value = 400, max.value = 500)
sum.dataset(uv.cassava.carot)## Dataset summary:
## Valid dataset
## Description: UV data for cassava
## Type of data: uvv-spectra
## Number of samples: 30
## Number of data points 101
## Number of metadata variables: 3
## Label of x-axis values: wavelength(nm)
## Label of data points: absorbance
## Number of missing values in data: 0
## Mean of data values: 0.127
## Median of data values: 0.02827
## Standard deviation: 0.2668
## Range of values: -0.0228 1.498
## Quantiles:
## 0% 25% 50% 75% 100%
## -0.02280 0.01033 0.02827 0.10888 1.49789Plotting spectra
plot.spectra(uv.cassava.carot, "varieties", legend="topleft")
Principal Components Analysis
Importance of components: Proportion of Variance explained in each component
uv.cassava.carot.pca = pca.analysis.dataset(uv.cassava.carot, scale = T, center = T)
summary(uv.cassava.carot.pca)## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Standard deviation 10.012 0.86277 0.1417 0.05031 0.02480 0.014 0.00975
## Proportion of Variance 0.992 0.00737 0.0002 0.00003 0.00001 0.000 0.00000
## Cumulative Proportion 0.992 0.99977 1.0000 0.99999 1.00000 1.000 1.00000
## PC8 PC9 PC10 PC11 PC12 PC13
## Standard deviation 0.00782 0.00479 0.00384 0.00307 0.003 0.00221
## Proportion of Variance 0.00000 0.00000 0.00000 0.00000 0.000 0.00000
## Cumulative Proportion 1.00000 1.00000 1.00000 1.00000 1.000 1.00000
## PC14 PC15 PC16 PC17 PC18 PC19
## Standard deviation 0.00207 0.00197 0.00189 0.00175 0.00169 0.00158
## Proportion of Variance 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000
## Cumulative Proportion 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000
## PC20 PC21 PC22 PC23 PC24 PC25
## Standard deviation 0.0015 0.00139 0.00133 0.00124 0.00116 0.00112
## Proportion of Variance 0.0000 0.00000 0.00000 0.00000 0.00000 0.00000
## Cumulative Proportion 1.0000 1.00000 1.00000 1.00000 1.00000 1.00000
## PC26 PC27 PC28 PC29 PC30
## Standard deviation 0.0011 0.000965 0.000929 0.000818 9.93e-16
## Proportion of Variance 0.0000 0.000000 0.000000 0.000000 0.00e+00
## Cumulative Proportion 1.0000 1.000000 1.000000 1.000000 1.00e+00PCAs Graphics
pca.scoresplot2D(uv.cassava.carot, uv.cassava.carot.pca, pcas = c(1,2), "varieties", ellipses="F")
pca.scoresplot2D(uv.cassava.carot, uv.cassava.carot.pca, pcas = c(1,2), "varieties", ellipses="T")
pca.scoresplot2D(uv.cassava.carot, uv.cassava.carot.pca, pcas = c(1,2), "colors", labels="F", pallette=2, ellipses="T")
Hierarchical Cluster Analysis
uv.cassava.carot.hc = clustering(uv.cassava.carot, method = "hc", distance = "euclidean")
dendrogram.plot(uv.cassava.carot, uv.cassava.carot.hc)
dendrogram.plot.col(uv.cassava.carot, uv.cassava.hc, "colors")
Profile and Quantification of Carotenoids using High Performance Liquid Chromatography (HPLC)
Subsequent analysis was performed to characterize the carotenoids by HPLC. The chromatographic analysis identified the cis-beta- and trans-beta-carotene, beta-carotene, lutein and beta-cryptoxanthin in all genotypes analyzed, confirmed the presence of lycopene only in Rosada genotype. Trans-beta-carotene was the major component in all samples.
setwd("/Users/Windows/Desktop/Miguel/Metabolomics-package")
load("hplcrodolfo.RData")
hplcrodolfo## Cultivar Lutein ßCryptoxanthin aCarotene cisßcarotene Transßcarotene
## 1 Aprontamesa 0.091 0.109 0.000 0.000 0.000
## 2 Pioneira 0.319 0.071 0.306 2.967 3.425
## 3 Oriental 0.052 0.103 0.000 0.109 0.123
## 4 Amarela 0.685 0.033 0.043 3.292 4.224
## 5 Catarina 0.357 0.076 0.198 4.770 5.797
## 6 IAC57670 0.688 0.000 0.664 5.826 6.420
## 7 Salezio 0.055 0.066 0.328 0.065 0.354
## 8 Estacao 0.058 0.084 0.435 0.254 0.328
## 9 Videira 0.070 0.110 0.000 0.039 0.340
## 10 Rosada 0.511 0.605 4.732 4.480 166.296
## Lycopene
## 1 0.000
## 2 0.000
## 3 0.000
## 4 0.000
## 5 0.000
## 6 0.000
## 7 0.000
## 8 0.000
## 9 0.000
## 10 1.534cultivar=factor(hplcrodolfo$Cultivar)
cultivar## [1] Aprontamesa Pioneira Oriental Amarela Catarina
## [6] IAC57670 Salezio Estacao Videira Rosada
## 10 Levels: Amarela Aprontamesa Catarina Estacao IAC57670 ... Videirahplc<-hplcrodolfo[2:7] Apply function of ade4
require(ade4)## Loading required package: ade4## Warning: package 'ade4' was built under R version 3.1.2HPLC <- dudi.pca(hplc, center = TRUE, scale = TRUE, scan = F,nf=5)
summary(HPLC) ##summarize the function## Class: pca dudi
## Call: dudi.pca(df = hplc, center = TRUE, scale = TRUE, scannf = F,
## nf = 5)
##
## Total inertia: 6
##
## Eigenvalues:
## Ax1 Ax2 Ax3 Ax4 Ax5
## 4.2593319 1.6109364 0.1050266 0.0240342 0.0006663
##
## Projected inertia (%):
## Ax1 Ax2 Ax3 Ax4 Ax5
## 70.98887 26.84894 1.75044 0.40057 0.01111
##
## Cumulative projected inertia (%):
## Ax1 Ax1:2 Ax1:3 Ax1:4 Ax1:5
## 70.99 97.84 99.59 99.99 100.00
##
## (Only 5 dimensions (out of 6) are shown)HPLC$eig ##eigenvalues (variability in the data)## [1] 4.259e+00 1.611e+00 1.050e-01 2.403e-02 6.663e-04 4.587e-06HPLC$li ##row cordinates## Axis1 Axis2 Axis3 Axis4 Axis5
## 1 1.0560 -0.9772 0.140625 -0.118353 0.0217429
## 2 0.4634 0.5304 -0.188201 -0.015570 0.0249376
## 3 1.0947 -1.0398 -0.001810 -0.093596 -0.0219527
## 4 0.2746 1.6941 0.742037 -0.111167 -0.0098409
## 5 0.2188 1.1486 -0.653985 -0.205050 -0.0173457
## 6 -0.1705 2.4414 -0.090789 0.220190 0.0069017
## 7 1.0822 -1.0046 0.007296 0.233256 -0.0540229
## 8 0.9711 -0.9825 -0.052789 0.213374 0.0401974
## 9 1.0649 -1.0249 0.070085 -0.120416 0.0103131
## 10 -6.0551 -0.7856 0.027531 -0.002668 -0.0009305HPLC$l1 ##row normed cordinates## RS1 RS2 RS3 RS4 RS5
## 1 0.5117 -0.7699 0.433923 -0.76342 0.84230
## 2 0.2246 0.4179 -0.580727 -0.10043 0.96606
## 3 0.5304 -0.8193 -0.005586 -0.60373 -0.85043
## 4 0.1330 1.3348 2.289686 -0.71707 -0.38123
## 5 0.1060 0.9050 -2.017986 -1.32265 -0.67196
## 6 -0.0826 1.9236 -0.280147 1.42031 0.26737
## 7 0.5244 -0.7915 0.022514 1.50459 -2.09281
## 8 0.4705 -0.7741 -0.162891 1.37634 1.55722
## 9 0.5160 -0.8075 0.216261 -0.77673 0.39952
## 10 -2.9340 -0.6189 0.084953 -0.01721 -0.03605HPLC$co ##column cordinates (correlations between variables and pcs)## Comp1 Comp2 Comp3 Comp4 Comp5
## Lutein -0.4767 0.8499 0.22431 -0.007895 0.0033189
## ßCryptoxanthin -0.9231 -0.3718 -0.00572 -0.096675 0.0147035
## aCarotene -0.9830 -0.1375 -0.02382 0.119013 0.0105172
## cisßcarotene -0.5326 0.8142 -0.23026 -0.018924 -0.0006946
## Transßcarotene -0.9867 -0.1608 0.01694 -0.008404 -0.0135522
## Lycopene -0.9780 -0.2063 0.02832 -0.005738 -0.0120158HPLC$c1 ##column normed scores (loadings)## CS1 CS2 CS3 CS4 CS5
## Lutein -0.2310 0.6697 0.69216 -0.05093 0.12857
## ßCryptoxanthin -0.4473 -0.2930 -0.01765 -0.62359 0.56960
## aCarotene -0.4763 -0.1083 -0.07350 0.76768 0.40743
## cisßcarotene -0.2581 0.6415 -0.71052 -0.12207 -0.02691
## Transßcarotene -0.4781 -0.1267 0.05226 -0.05421 -0.52500
## Lycopene -0.4739 -0.1625 0.08738 -0.03701 -0.46548Plot PCA
biplot(HPLC)